Interest-aware content discovery in peer-to-peer social networks.

Journal article


Guo, Yonghong, Liu, Lu, Wu, Yan and Hardy, J. 2018. Interest-aware content discovery in peer-to-peer social networks. ACM Transactions on Internet Technology.
AuthorsGuo, Yonghong, Liu, Lu, Wu, Yan and Hardy, J.
Abstract

With the increasing popularity and rapid development of Online Social Networks (OSNs), OSNs not only bring fundamental changes to information and communication technologies, but also make extensive and profound impact on all aspects of our social life. Efficient content discovery is a fundamental challenge for large-scale distributed OSNs. However, the similarity between social networks and online social networks leads us to believe that the existing social theories are useful for improving the performance of social content discovery in online social networks. In this paper, we propose an interest-aware social-like peer-to-peer (IASLP) model for social content discovery in OSNs by mimicking ten different social theories and strategies. In the IASLP network, network nodes with similar interests can meet, help each other and co-operate autonomously to identify useful contents. The presented model has been evaluated and simulated in a dynamic environment with an evolving network. The experimental results show that the recall of IASLP is 20% higher than the existing method SESD while the overhead is 10% lower. The IASLP can generate higher flexibility and adaptability and achieve better performance than the existing methods.

KeywordsOnline social networks; Content discovery; Self-organization
Year2018
JournalACM Transactions on Internet Technology
PublisherAssociation for Computing Machinery
ISSN15335399
Web address (URL)http://hdl.handle.net/10545/622064
hdl:10545/622064
Publication dates01 May 2018
Publication process dates
Deposited22 Jan 2018, 14:07
Accepted01 Dec 2017
ContributorsUniversity of Derby
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